Praedicere Possumus: An Italian web-based application for predictive microbiology to ensure food safety

  • Pierluigi Polese Polytechnic Department of Engineering and Architecture, University of Udine, Italy.
  • Manuela Del Torre Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Italy.
  • Mara Lucia Stecchini | mara.stecchini@uniud.it Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Italy.

Abstract

The use of predictive modelling tools, which mainly describe the response of microorganisms to a particular set of environmental conditions, may contribute to a better understanding of microbial behaviour in foods. In this paper, a tertiary model, in the form of a readily available and userfriendly web-based application Praedicere Possumus (PP) is presented with research examples from our laboratories. Through the PP application, users have access to different modules, which apply a set of published models considered reliable for determining the compliance of a food product with EU safety criteria and for optimising processing throughout the identification of critical control points. The application pivots around a growth/no-growth boundary model, coupled with a growth model, and includes thermal and non-thermal inactivation models. Integrated functionalities, such as the fractional contribution of each inhibitory factor to growth probability (f) and the time evolution of the growth probability (Pt), have also been included. The PP application is expected to assist food industry and food safety authorities in their common commitment towards the improvement of food safety.

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Published
2018-04-09
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Section
Original Articles
Supporting Agencies
MIUR Project “Proof of Concept Network” (PoCN), AREA Science Park (Trieste, Italy)
Keywords:
Predictive microbiology, Modelling food, Food safety, Praedicere Possumus, Web application
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How to Cite
Polese, P., Del Torre, M., & Stecchini, M. L. (2018). Praedicere Possumus: An Italian web-based application for predictive microbiology to ensure food safety. Italian Journal of Food Safety, 7(1). https://doi.org/10.4081/ijfs.2018.6943